AB1667 UNDERSTANDING THE ROLE AND ADOPTION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN RHEUMATOLOGY RESEARCH: AN IN-DEPTH REVIEW OF THE LITERATURE
Authors: A. Madrid García, B. Merino Barbancho, A. Rodríguez González, B. Fernandez, L. León, L. Abasolo, D. Freites, E. Menasalvas Ruiz, L. Rodriguez Rodriguez
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Artificial intelligence (AI) is rapidly gaining adoption in the medical field [1]. We review, the original research articles that combine AI and Rheumatic and Musculoskeletal diseases (RMDs) in a five-year period (2017-2021) and classify them into one of the following categories: disease classification, disease prediction, predictors identification, patient stratification and disease subtype identification, disease progression and activity, and treatment response. <h3>Objectives</h3> The present review arises with the aim of characterizing how rheumatology researchers are employing AI techniques and to study to what extent these techniques have been adopted by rheumatologists. <h3>Methods</h3> A literature search was conducted to identify publications related to RMDs in which AI techniques played a relevant role in four different sources: PubMed, Web of Science, Scopus and in the search engines of rheumatology journals classified as Q1 and Q2 according to Journal Citation Reports 2019. The keywords employed were artificial intelligence, supervised learning, unsupervised learning, deep learning, big data, data mining, machine learning, rheumatology, musculoskeletal diseases, rheumatic. Only original research articles related to RMDs with a PubMed identification number (PMID), not duplicated, written in English, in which there was not animal involvement and in which at least one AI technique was employed, fulfilled the inclusion criteria. Later, an abstract and title filtering followed by a full text review of selected articles was performed. <h3>Results</h3> After applying the inclusion and exclusion criteria (see Figure 1) and performing the full text review, 91 articles remained. From those, the article title, the identifiers (DOI, PMID), the journal, the publication year, the disease, the algorithms/techniques, the programming language, the number of patients, the validation type, the objective, and the authorship information were collected. In addition, an illustrative table with more than twenty different RMDs, including arthritis, vasculitis, fibromyalgia, gout, spondylitis, sarcopenia, Sjögren, systemic sclerosis, and back and neck pain was obtained. The publication trend has increased each year, from 20% to 48%, in relation to the previous year. The techniques employed are variable, including supervised and unsupervised learning, and traditional machine learning and deep learning approaches. Some of the topics addressed are classification, regression, clustering, dimensionality reduction, topic modelling, computer vision, reinforcement learning, few-shot learning, and trajectory analyses. Osteoarthritis, rheumatoid arthritis, systemic lupus erythematosus and osteoporosis are the RMDs that accounts for most of the research studies in which AI techniques are used more assiduously. However, AI is also playing a key role in rare diseases research, such as vasculitis, with approaches such as topic modelling. The amount of data in the studies reviewed is variable, from just hundreds of observations to hundreds of thousands, showing that "big data" cohorts are not mandatory to take advantage of such techniques. <h3>Conclusion</h3> The outstanding and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which AI plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. We can conclude that the rheumatology research community is increasingly adopting novel AI techniques. <h3>Reference</h3> [1]Martinez-Millana, A., Saez-Saez, A., Tornero-Costa, R., Azzopardi-Muscat, N., Traver, V., &amp; Novillo-Ortiz, D. (2022). Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. International Journal of Medical Informatics, 166, 104855. https://doi.org/https://doi.org/10.1016/j.ijmedinf.2022.104855 <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.